Column

Chart A

Column

Chart B

Chart C

---
title: "Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library("p8105.datasets")
library(plotly)

data("instacart")
data("ny_noaa")
data("rest_inspec")
```


```{r}
# nyc_airbnb <- 
#   nyc_airbnb %>%
#   mutate(rating = review_scores_location / 2) %>%
#   select(neighbourhood_group, neighbourhood, rating, price, room_type, lat, long) %>%
#   filter(
#     neighbourhood_group == "Manhattan",
#     price %in% 100:500,
#     room_type == "Entire home/apt"
#   ) %>%
#   drop_na(rating)
# 
# instacart2 <-
#   instacart %>%
#   filter(department == "alcohol") %>%
#   group_by(product_name) %>%
#   summarize(n = n())
# 
# instacart %>% group_by(department) %>%
#   summarize(n = n())
# 
# instacart %>% count(aisle) %>% arrange(desc(n))
# 
# instacart2 <- 
#   instacart %>%
#   filter(department == "alcohol") %>%
#   #group_by(aisle) %>%
#   count(product_name) %>%
#   mutate(rank = min_rank(rank(desc(n)))) %>%
#   filter(rank <= 50) %>%
#   arrange(desc(n))
```

Column {data-width=650}
-----------------------------------------------------------------------

### Chart A

```{r}
# instacart %>%
#   filter(department == "alcohol") %>%
#   select(product_name, order_dow, order_hour_of_day) %>%
#   plot_ly(
#     x = ~product_name, y = ~count(product_name),
#     alpha = 0.5, type = "scatter", mode = "markers") 
# 
# instacart %>%
#   plot_ly(
#     x = 
#   )
# 
# ?plot_ly
```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart B

```{r}
# nyc_airbnb %>%
#   mutate(neighbourhood = fct_reorder(neighbourhood, price)) %>%
#   plot_ly(
#     y = ~price, x = ~neighbourhood, color = ~neighbourhood,
#     type = "box", colors = "viridis")
```

### Chart C

```{r}
instacart %>% 
  filter(department == "alcohol") %>%
  count(product_name, aisle) %>% 
  mutate(rank = min_rank(rank(desc(n)))) %>%
  filter(rank <= 50) %>%
  mutate(product_name = fct_reorder(product_name, n)) %>% 
  plot_ly(x = ~product_name, y = ~n, color = ~aisle, type = "bar", colors = "viridis")
```